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As a kind of intelligent parking mode, shared parking mode has been widely promoted, and parking app, as a product of the development of sharing economy in the field of transportation, is also being vigorously advocated. However, there are not many people who use parking app to find and reserve parking space in practice, which is largely caused by insufficient information provided by the parking apps. In order to better explain, predict and improve drivers’ choice of parking apps, a multinomial logit model was established to analyze the relationship between drivers’ parking app choice behavior and the influential factors. The influential factors include drivers’ individual characteristics and parking app’s attributes, which were extracted from a questionnaire and typical parking apps currently in operation. The results show that the reservation and shared parking spaces, available parking spaces, parking charges and distance to the destination are the main factors that determine the drivers’ choice of parking app. This paper provides a reference for the development of Ningbo parking apps.
Xiaofei Ye; Chang Yang; Tao Wang; Xingchen Yan; Song Li; Jun Chen. Research on parking app choice behavior based on MNL. Travel Behaviour and Society 2021, 25, 174 -182.
AMA StyleXiaofei Ye, Chang Yang, Tao Wang, Xingchen Yan, Song Li, Jun Chen. Research on parking app choice behavior based on MNL. Travel Behaviour and Society. 2021; 25 ():174-182.
Chicago/Turabian StyleXiaofei Ye; Chang Yang; Tao Wang; Xingchen Yan; Song Li; Jun Chen. 2021. "Research on parking app choice behavior based on MNL." Travel Behaviour and Society 25, no. : 174-182.
Cycling is an increasingly popular mode of transport as part of the response to air pollution, urban congestion, and public health issues. The emergence of bike sharing programs and electric bicycles have also brought about notable changes in cycling characteristics, especially cycling speed. In order to provide a better basis for bicycle-related traffic simulations and theoretical derivations, the study aimed to seek the best distribution for bicycle riding speed considering cyclist characteristics, vehicle type, and track attributes. K-means clustering was performed on speed subcategories while selecting the optimal number of clustering using L method. Then, 15 common models were fitted to the grouped speed data and Kolmogorov–Smirnov test, Akaike information criterion, and Bayesian information criterion were applied to determine the best-fit distribution. The following results were acquired: (1) bicycle speed sub-clusters generated by the combinations of bicycle type, bicycle lateral position, gender, age, and lane width were grouped into three clusters; (2) Among the common distribution, generalized extreme value, gamma and lognormal were the top three models to fit the three clusters of speed dataset; and (3) integrating stability and overall performance, the generalized extreme value was the best-fit distribution of bicycle speed.
Xingchen Yan; Xiaofei Ye; Jun Chen; Tao Wang; Zhen Yang; Hua Bai. Bicycle Speed Modelling Considering Cyclist Characteristics, Vehicle Type and Track Attributes. World Electric Vehicle Journal 2021, 12, 43 .
AMA StyleXingchen Yan, Xiaofei Ye, Jun Chen, Tao Wang, Zhen Yang, Hua Bai. Bicycle Speed Modelling Considering Cyclist Characteristics, Vehicle Type and Track Attributes. World Electric Vehicle Journal. 2021; 12 (1):43.
Chicago/Turabian StyleXingchen Yan; Xiaofei Ye; Jun Chen; Tao Wang; Zhen Yang; Hua Bai. 2021. "Bicycle Speed Modelling Considering Cyclist Characteristics, Vehicle Type and Track Attributes." World Electric Vehicle Journal 12, no. 1: 43.
With the rapid development and convenient service of online car-hailing, it has gradually become the preferred choice for people to travel. Accurate forecasting of car-hailing trip demand not only enables the drivers and companies to dispatch the vehicles and increase the mileage utilization, but also reduces the passengers’ waiting-time. The rebalance of spatiotemporal demand and supply could mitigate traffic congestion, reduce traffic emission, and guide people’s travel patterns. This study aimed to develop a short-term demand forecasting model for car-hailing services using stacking ensemble learning approach. The spatial-temporal characteristics of online car-hailing demand were analyzed and extracted through data analysis. The region-level spatial characteristics, time features, and weather conditions were added into the forecasting model. Then the stacking ensemble learning model was developed to predict the car-hailing demand at region-level for different time intervals, including 10 min, 15 min, and 30 min. The validation results suggested that the proposed stacking ensemble learning model has reasonable good prediction accuracy for different time intervals. The comparison results show that the short-term demand forecasting model based on stacking ensemble learning is better than single LSTM, SVR, lightGBM and Random Forest models. MAE and RMSE increased by 6.0% and 5.2% respectively at 30 min time interval, which further verifies the effectiveness and feasibility of the proposed model.
Yuming Jin; Xiaofei Ye; Qiming Ye; Tao Wang; Jun Cheng; Xingchen Yan. Demand Forecasting of Online Car-Hailing With Stacking Ensemble Learning Approach and Large-Scale Datasets. IEEE Access 2020, 8, 199513 -199522.
AMA StyleYuming Jin, Xiaofei Ye, Qiming Ye, Tao Wang, Jun Cheng, Xingchen Yan. Demand Forecasting of Online Car-Hailing With Stacking Ensemble Learning Approach and Large-Scale Datasets. IEEE Access. 2020; 8 ():199513-199522.
Chicago/Turabian StyleYuming Jin; Xiaofei Ye; Qiming Ye; Tao Wang; Jun Cheng; Xingchen Yan. 2020. "Demand Forecasting of Online Car-Hailing With Stacking Ensemble Learning Approach and Large-Scale Datasets." IEEE Access 8, no. : 199513-199522.
To provide a knowledge basis for updating the design speed in bicycle facility codes, this paper examines factors that influence bicycle free-flow speed. We investigated six segments of Nanjing’s separated bicycle lane and established a generalized linear model of the relationship between bicycle free-flow speed and bicyclists’ gender, age, bicycle type, lane width, bicycle lateral position, and travel period. With the model, we determined the statistical significance of each factor and assessed each factor’s impact extent. Through comparing the 85th percentile speeds of different groups, we proposed the recommended values and a method for calculating the design speed of separate bicycle lanes. The following results and conclusions were obtained: (1) The significant influential factors of bicycle free-flow speed were bicyclists’ gender and age, bicycle type, lane width, and bicycles’ lateral position. (2) Bicycle type had the greatest impact on bicycle free-flow speed, following by bicycle lateral position, gender, age, and lane width in sequence. (3) The recommended design speeds for separate lanes of less than 3.5 m and the wider lanes were 25 km/h and 30 km/h, respectively.
Xingchen Yan; Jun Chen; Hua Bai; Tao Wang; Zhen Yang. Influence Factor Analysis of Bicycle Free-Flow Speed for Determining the Design Speeds of Separated Bicycle Lanes. Information 2020, 11, 459 .
AMA StyleXingchen Yan, Jun Chen, Hua Bai, Tao Wang, Zhen Yang. Influence Factor Analysis of Bicycle Free-Flow Speed for Determining the Design Speeds of Separated Bicycle Lanes. Information. 2020; 11 (10):459.
Chicago/Turabian StyleXingchen Yan; Jun Chen; Hua Bai; Tao Wang; Zhen Yang. 2020. "Influence Factor Analysis of Bicycle Free-Flow Speed for Determining the Design Speeds of Separated Bicycle Lanes." Information 11, no. 10: 459.
Reliable short-term prediction of available parking space (APS) is the basic theory of parking guidance information system (PGIS). Based on the Intelligent parking system at the Eastern New Town, Yinzhou District, Ningbo, China, this study collected the data of parking availability in the on-street parking areas. The variation characteristics of APS were investigated and analyzed at different spatial-temporal levels. Then the APS prediction models based on Gradient Boosting Decision Tree (GBDT) and Wavelet Neural Network (WNN) were proposed. Furthermore, an improved WNN algorithm with (WA) decomposition and Particle Swarm Optimization (PSO) were presented. The original time series was decomposed and reconstructed by wavelet analysis, and the WNN algorithm found the optimal threshold of initial weight through PSO. The result of GBDT (weekday: MSE=27.37, SMSE=0, TIME=35min, weekend: MSE=9.9, SMSE=0,TIME=35min ) and WA-PSO-WNN (weekday: MSE=14.93,SMSE=1.88, TIME=160.32s, weekend: MSE=12.33, SMSE=10.23, TIME=160.95s) approximated the true value. But the prediction time of GBDT was too long to be applicable to the short-term prediction of APS in this paper. Compared with the methods of GBDT, WNN, and PSO-WNN, the WA-PSO-WNN algorithm performs much better. The average differences in MSE between WA-PSO-WNN and GBDT for weekday and weekend data are 45.45% and 58.76%, respectively, indicating that WA-PSO-WNN can increase the prediction accuracy of weekday and weekend data by an average of 45.45% and 58.76% compared with the GBDT model. Finally, the application prospects of short-term APS forecasting were also discussed in reducing cruising parking behavior, reducing illegal parking behavior and adjusting dynamic parking rates to verify the importance of APS short-term forecasting.
Xiaofei Ye; Jinfen Wang; Tao Wang; Xingchen Yan; Qiming Ye; Jun Chen. Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches. IEEE Access 2020, 8, 174530 -174541.
AMA StyleXiaofei Ye, Jinfen Wang, Tao Wang, Xingchen Yan, Qiming Ye, Jun Chen. Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches. IEEE Access. 2020; 8 (99):174530-174541.
Chicago/Turabian StyleXiaofei Ye; Jinfen Wang; Tao Wang; Xingchen Yan; Qiming Ye; Jun Chen. 2020. "Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches." IEEE Access 8, no. 99: 174530-174541.
Shared parking schemes are not commonly implemented in residential areas due to the uncertainty and conflicts associated with the benefits of such schemes for stakeholders, namely, parking suppliers, parking managers, and the public. To evaluate the economic and social impacts of shared parking in residential areas on its stakeholders, the risk and benefit factors were determined through influential analysis and a questionnaire. A risk–benefit model was established to quantify the risks and benefits for stakeholders. The social return on investment and sensitivity analysis were applied to estimate the economic feasibility of shared parking in residential areas. The methodology combined the use of qualitative, quantitative, and financial information gathered and analyzed to estimate the “value” of shared parking, including its risks, benefits, management pressure, and social benefit. The model was calibrated using the survey data collected from the city of Ningbo in China. The results showed that: (1) The net present value was negative, indicating that the benefits of shared parking were lower than the risks, and thus this scheme would not be economically feasible in residential areas. (2) The cost of purchasing new equipment and rebuilding parking lots had the greatest impact on the benefits of shared parking in residential areas, with a sensitivity coefficient of 4.396, followed by the income from shared parking charges (3.885), and the salary of parking managers (3.619). (3) If the income from parking charges and the salary of parking managers were more than 69,408.5 and 31,091.1 yuan per month, respectively, and the cost of improving parking infrastructure was less than 14,003.2 yuan per month, residential areas could obtain additional benefits due to the acceptance of a shared parking scheme. This study provides theoretical support for the reasonable determination of the costs, risks, and benefits associated with participating in a shared parking scheme in a residential area.
Xiaofei Ye; Xinliu Sui; Jin Xie; Tao Wang; Xingchen Yan; Jun Chen. Assessment of the Economic and Social Impact of Shared Parking in Residential Areas. Information 2020, 11, 411 .
AMA StyleXiaofei Ye, Xinliu Sui, Jin Xie, Tao Wang, Xingchen Yan, Jun Chen. Assessment of the Economic and Social Impact of Shared Parking in Residential Areas. Information. 2020; 11 (9):411.
Chicago/Turabian StyleXiaofei Ye; Xinliu Sui; Jin Xie; Tao Wang; Xingchen Yan; Jun Chen. 2020. "Assessment of the Economic and Social Impact of Shared Parking in Residential Areas." Information 11, no. 9: 411.
In order to improve the adaptation of driver to the advanced driver assistance system (ADAS) and optimize the active safety control technology of vehicle under man-computer cooperative driving, this paper investigated the correlation between driver’s improper driving behaviors and abnormal vehicle states under the ADAS. Based on the warning data collected from the driver’s assistance warning system equipped on buses, the interaction between improper behaviors, between abnormal vehicle states, and between improper behaviors and abnormal vehicle states were quantitatively analyzed through the hierarchical clustering method and improved Apriori algorithm. The results showed that eye closure and yawn were high in concurrency (probability: 0.888) and interaction (average probability: 0.946); the interaction among lane departure, rapid acceleration, and rapid deceleration are frequent (average probability: 0.7224); eye closure (average probability: 0.452) and yawn (average probability: 0.444) are likely to induce abnormal vehicle states such as rapid acceleration and rapid deceleration. Some suggestions proposed based on the results are as follows. First, it is suggested that the ADAS should combine the warning modes of eye closure and yawn; second, when the driver closes eyes or yawns, the control of the ADAS over the lateral and longitudinal performance of vehicle should be enhanced; third, the extent of control by the ADAS should be determined according to the relationship probability; finally, the lateral control over the vehicle by the ADAS should be strengthened when there is a forward collision warning.
Tao Wang; Yuzhi Chen; Xingchen Yan; Jun Chen; Wenyong Li. The Relationship between Bus Drivers’ Improper Driving Behaviors and Abnormal Vehicle States Based on Advanced Driver Assistance Systems in Naturalistic Driving. Mathematical Problems in Engineering 2020, 2020, 1 -12.
AMA StyleTao Wang, Yuzhi Chen, Xingchen Yan, Jun Chen, Wenyong Li. The Relationship between Bus Drivers’ Improper Driving Behaviors and Abnormal Vehicle States Based on Advanced Driver Assistance Systems in Naturalistic Driving. Mathematical Problems in Engineering. 2020; 2020 ():1-12.
Chicago/Turabian StyleTao Wang; Yuzhi Chen; Xingchen Yan; Jun Chen; Wenyong Li. 2020. "The Relationship between Bus Drivers’ Improper Driving Behaviors and Abnormal Vehicle States Based on Advanced Driver Assistance Systems in Naturalistic Driving." Mathematical Problems in Engineering 2020, no. : 1-12.
With the strengthening of environmental awareness, the government pays much more attention to environmental protection and thus implements carbon trading schemes to promote the reduction of global carbon dioxide emissions. The carbon Generalized System of Preferences (GSP) is an incentive mechanism for citizens to value their energy conservation and carbon reduction. Individual travel needs to rely on various means of transportation, resulting in energy consumption. Carbon tax or subsidy can only be carried out after carbon GSP accurately measures individual carbon emissions. The big data acquired from the smart cards of passengers’ travels provide the possibility for carbon emission accounting of individual travel. This research proposes a carbon emission measurement of individual travel. Through establishing the network model of the Nanjing metro with a complex method, the shortest path of the passengers’ travels is obtained. Combined with the origination–destination (OD) records of the smart cards, the total distance of the passengers’ travels is obtained. By selecting the operation table to estimate the carbon emissions generated by the daily operation of the subway system, the carbon emissions per kilometer or per time of passenger travel are finally obtained. With the accurate tracking of carbon emissions for individual travel, the government may establish a comprehensive monitoring system so as to establish a carbon tax and carbon supplement mechanism for citizens.
Wei Yu; Tao Wang; Yujie Xiao; Jun Chen; Xingchen Yan. A Carbon Emission Measurement Method for Individual Travel Based on Transportation Big Data: The Case of Nanjing Metro. International Journal of Environmental Research and Public Health 2020, 17, 5957 .
AMA StyleWei Yu, Tao Wang, Yujie Xiao, Jun Chen, Xingchen Yan. A Carbon Emission Measurement Method for Individual Travel Based on Transportation Big Data: The Case of Nanjing Metro. International Journal of Environmental Research and Public Health. 2020; 17 (16):5957.
Chicago/Turabian StyleWei Yu; Tao Wang; Yujie Xiao; Jun Chen; Xingchen Yan. 2020. "A Carbon Emission Measurement Method for Individual Travel Based on Transportation Big Data: The Case of Nanjing Metro." International Journal of Environmental Research and Public Health 17, no. 16: 5957.
The effective assessment of drivers’ driving capability under the condition of an advanced driver assistance system is of great significance to the precise switching of driving rights between human and machine and the promotion of the development of man-computer collaboration. In this study, real-time collected warning data from bus driver state monitoring system (DSMS) and advanced driver assistance system (ADAS) were utilized to determine the drivers’ comprehensive driving capability indicators. The information utility and interaction of the indicators were considered, and an integrated weight method based on standard deviations was proposed. This method was used to combine the entropy weight method and improved analytic network process (ANP), to evaluate the drivers’ comprehensive driving capability under man-computer cooperative driving conditions in real time. The results show that the entropy weight method and improved ANP algorithm have good consistency and are significantly correlated and that the integrated weight method is effective and dependable. The top four indicators in the integrated weighting results were eye closure (0.241), yawn (0.210), rapid deceleration (0.186), and lane departure (0.159). Drivers' comprehensive driving capability scores were concentrated in the score range of 1 to 6, with the lowest scores in zones A and B for stages 2, 11 and 21. Therefore, it is necessary to further explore the relationship between driver behavior, vehicle status and road traffic environment within the score range of 1 to 6 so that the man-computer interaction can be optimized and the driver's comprehensive driving capability can be improved.
Tao Wang; Yuzhi Chen; Xingchen Yan; Wenyong Li; Dong Shi. Assessment of Drivers’ Comprehensive Driving Capability Under Man-Computer Cooperative Driving Conditions. IEEE Access 2020, 8, 152909 -152923.
AMA StyleTao Wang, Yuzhi Chen, Xingchen Yan, Wenyong Li, Dong Shi. Assessment of Drivers’ Comprehensive Driving Capability Under Man-Computer Cooperative Driving Conditions. IEEE Access. 2020; 8 (99):152909-152923.
Chicago/Turabian StyleTao Wang; Yuzhi Chen; Xingchen Yan; Wenyong Li; Dong Shi. 2020. "Assessment of Drivers’ Comprehensive Driving Capability Under Man-Computer Cooperative Driving Conditions." IEEE Access 8, no. 99: 152909-152923.
This study aimed to explore the effects of type and specifications of bus stop on bicycle speed and cycle track capacity. This paper investigates the traffic flow operations of tracks at basic sections, curbside stops, and bus bays by video recording. T-test and comparative study were used to analyze the influences of stop types on bicycle speed and capacity of track. The relationships between stop specifications and speed and capacity of track are analyzed with correlation analysis. The main results are as follows: (1) Without passengers crossing, bus bays have significant impact on bicycle speed, while it is not for curbside stops; (2) except platform length, there are strong negative relationships between bicycle speed and density of platform access, total width of platform accesses (TWPA), total width of platform accesses-to-platform length ratio (TWPA-to-PL ratio), total width of platform accesses-to-track width ratio (TWPA-to-TW ratio); (3) curbside stop and bus bay reduce track capacities by 32% and 13.5% on average, respectively; and (4) in contrast to bus bays, curbside stops have more significant impact on capacity of track, which also presents in the influence of the setting parameters of stops. Based the results above, some suggestions on stop specifications are finally proposed.
Xingchen Yan; Jun Chen; Xiaofei Ye; Tao Wang; Zhen Yang; Hua Bai. Studying the Influences of Bus Stop Type and Specifications on Bicycle Flow and Capacity for Better Bicycle Efficiency. Information 2020, 11, 370 .
AMA StyleXingchen Yan, Jun Chen, Xiaofei Ye, Tao Wang, Zhen Yang, Hua Bai. Studying the Influences of Bus Stop Type and Specifications on Bicycle Flow and Capacity for Better Bicycle Efficiency. Information. 2020; 11 (8):370.
Chicago/Turabian StyleXingchen Yan; Jun Chen; Xiaofei Ye; Tao Wang; Zhen Yang; Hua Bai. 2020. "Studying the Influences of Bus Stop Type and Specifications on Bicycle Flow and Capacity for Better Bicycle Efficiency." Information 11, no. 8: 370.
To identify and quantify the factors that influence the risky riding behaviors of electric bike riders, we designed an e-bike rider behavior questionnaire (ERBQ) and obtained 573 valid samples through tracking surveys and random surveys. An exploratory factor analysis was then conducted to extract four scales: riding confidence, safety attitude, risk perception, and risky riding behavior. Based on the exploratory factor analysis, a structural equation model (SEM) of electric bike riding behaviors was constructed to explore the intrinsic causal relationships among the variables that affect the risky e-bike riding behavior. The results show that the relationship between riding confidence and risky riding behavior is mediated by risk perception and safety attitudes. Safety attitude was found to be significantly associated with risky riding behaviors. Specifically, herd mentality is most closely related to safety attitudes, which means that those engaged in e-bike traffic management and safety education should pay special attention to riders’ psychological management and education. Risk perception has a direct path to risky riding behaviors. Specifically, stochastic evaluation and concern degree are significantly related to e-bike riders’ risk perception. The findings of this study provide an empirical basis for the creation of safety interventions for e-bike riders in China.
Tao Wang; Sihong Xie; Xiaofei Ye; Xingchen Yan; Jun Chen; Wenyong Li. Analyzing E-Bikers’ Risky Riding Behaviors, Safety Attitudes, Risk Perception, and Riding Confidence with the Structural Equation Model. International Journal of Environmental Research and Public Health 2020, 17, 4763 .
AMA StyleTao Wang, Sihong Xie, Xiaofei Ye, Xingchen Yan, Jun Chen, Wenyong Li. Analyzing E-Bikers’ Risky Riding Behaviors, Safety Attitudes, Risk Perception, and Riding Confidence with the Structural Equation Model. International Journal of Environmental Research and Public Health. 2020; 17 (13):4763.
Chicago/Turabian StyleTao Wang; Sihong Xie; Xiaofei Ye; Xingchen Yan; Jun Chen; Wenyong Li. 2020. "Analyzing E-Bikers’ Risky Riding Behaviors, Safety Attitudes, Risk Perception, and Riding Confidence with the Structural Equation Model." International Journal of Environmental Research and Public Health 17, no. 13: 4763.
With the concept of sharing economic entering into our lives, many parking Apps are designed for connecting the drivers and vacated parking spaces. However, there are not many drivers who use the mobile Apps to reserve and find available parking spaces, which is largely due to the insufficient information provided by the parking App. In order to better explain, predict, and improve drivers’ acceptance of parking App, the conceptual framework based on technology acceptance model was developed to establish the relationships between the drivers’ intention to accept parking App, trust in parking App, perceived usefulness of parking App, and perceived ease of its use. Then structural equation model was established to analyze the relationship between various variables. The results show that the trust in parking App, perceived usefulness, perceived ease of use, and parking App attributes are the main factors that determine the intention to use parking App. Through the test of direct effect, indirect effect, and total effect in the model, it is found that perceived usefulness has the largest total impact on acceptance intention, with a standardized coefficient of 0.984, followed by parking App attribute (0.743), perceived ease of use (0.384), and trust in parking App (0.381).
Chang Yang; Xiaofei Ye; Jin Xie; Xingchen Yan; Lili Lu; Zhen Yang; Tao Wang; Jun Chen. Analyzing Drivers’ Intention to Accept Parking App by Structural Equation Model. Journal of Advanced Transportation 2020, 2020, 1 -11.
AMA StyleChang Yang, Xiaofei Ye, Jin Xie, Xingchen Yan, Lili Lu, Zhen Yang, Tao Wang, Jun Chen. Analyzing Drivers’ Intention to Accept Parking App by Structural Equation Model. Journal of Advanced Transportation. 2020; 2020 ():1-11.
Chicago/Turabian StyleChang Yang; Xiaofei Ye; Jin Xie; Xingchen Yan; Lili Lu; Zhen Yang; Tao Wang; Jun Chen. 2020. "Analyzing Drivers’ Intention to Accept Parking App by Structural Equation Model." Journal of Advanced Transportation 2020, no. : 1-11.
Cruising for parking creates a moving queue of cars that are waiting for vacated parking spaces, but no one can see how many cruisers are in the queue because they are mixed in with normal cars that are actually going somewhere. In order to mitigate the influence of cruising for parking on the normal cars, the park-and-visit cruising tests with GPS and cameras was applied to collect the behavior of the cruisers, and the videotapes of traffic flows were used to measure the volume of cruising cars and the traffic status of normal cars, simultaneously. On this basis, a parking time model based on proportional hazard-based duration model was proposed, and the factors affecting cruise for parking were analyzed, including the volume, search time, speed, acceleration, lane-change frequency, and distracted time of the cruising car. The multiple linear regression model was also established to compare with proportional hazard-based duration model results. The results indicated that between 9 and 56 percent of the traffic was cruising for parking, and the average search time was about 6.03 min. The low-speed, volume, high acceleration frequency, and lane-change times of cruising cars have a negative effect on shortening travel time of the normal traffic flow. Conversely, high-speed of cruising cars has a positive effect on shortening travel time of traffic flow. Moreover, travel time changes in varying degrees due to various factors. Under postulated conditions, the model can be used to estimate the travel time. It is hoped that this study will contribute to improve the planning and management of cruising for parking.
Yating Zhu; Xiaofei Ye; Jun Chen; Xingchen Yan; Tao Wang. Impact of Cruising for Parking on Travel Time of Traffic Flow. Sustainability 2020, 12, 3079 .
AMA StyleYating Zhu, Xiaofei Ye, Jun Chen, Xingchen Yan, Tao Wang. Impact of Cruising for Parking on Travel Time of Traffic Flow. Sustainability. 2020; 12 (8):3079.
Chicago/Turabian StyleYating Zhu; Xiaofei Ye; Jun Chen; Xingchen Yan; Tao Wang. 2020. "Impact of Cruising for Parking on Travel Time of Traffic Flow." Sustainability 12, no. 8: 3079.
Due to the lack of adjustment index systems for taxi fleet sizes in China, this paper used the taxi operating datasets from Ningbo City and established a regression tree model to consider the endogenous indicators that affect taxi fleet sizes. Then, a dynamic adjustment mechanism of taxi fleet sizes was proposed by combining the exogenous and endogenous indicators. The importance of the exogenous and endogenous indicators was sorted using the Delphi method. The threshold value of each indicator was also given. The results indicated that (1) in the three-layer structure of the regression tree model, the mileage utilization had the strongest effect on the fleet size of taxis, and the F statistic was 63.73; followed by the average daily revenue of a single taxi, the average waiting time to catch a single taxi, the average operating time of a single taxi, and the revenue per 100 km. The overall accuracy of the model was found to be valid. (2) When the mileage utilization was less than 0.6179 and the average daily revenue of a single taxi was less than 798.38 Yuan, the fleet size of cruising taxis was surplus and should be reduced by 362 vehicles. (3) When the mileage utilization was more than 0.6774 and the average waiting time to catch a single taxi was more than 259.09 s, the fleet size of cruising taxis was insufficient, and we suggest an increase of 463 taxis.
Xiaofei Ye; Min Li; Zhongzhen Yang; Xingchen Yan; Jun Chen. A Dynamic Adjustment Model of Cruising Taxicab Fleet Size Combined the Operating and Flied Survey Data. Sustainability 2020, 12, 2776 .
AMA StyleXiaofei Ye, Min Li, Zhongzhen Yang, Xingchen Yan, Jun Chen. A Dynamic Adjustment Model of Cruising Taxicab Fleet Size Combined the Operating and Flied Survey Data. Sustainability. 2020; 12 (7):2776.
Chicago/Turabian StyleXiaofei Ye; Min Li; Zhongzhen Yang; Xingchen Yan; Jun Chen. 2020. "A Dynamic Adjustment Model of Cruising Taxicab Fleet Size Combined the Operating and Flied Survey Data." Sustainability 12, no. 7: 2776.
The location and grade setting of a timber logistics center is an important link in the optimization of the timber logistics system, the rationality of which can effectively improve the efficiency of the timber logistics supply chain. There is a long distance between the main forested areas in China, and more than 55% of the timber demand depends on imports. Research and practice of systematically planning timber logistics centers in the whole country have not been well carried out, which reduces the efficiency of timber logistics. In this paper, 47 timber trading markets with a certain scale in China are selected as the basis for logistics center selection. Based on their transportation network relationship and the number of enterprises in the market, combined with the complex network theory and data analysis method, the network characteristics of three different transportation networks are measured. After determining the transportation capacity indicator, the logistics capacity coefficient is measured based on the freight volume of each node. Then, the important nodes are identified, and each node is graded to systematically set up the timber logistics center.
Liang Xue; Xin Huang; Yuchun Wu; Xingchen Yan; Yan Zheng. Grade Setting of a Timber Logistics Center Based on a Complex Network: A Case Study of 47 Timber Trading Markets in China. Information 2020, 11, 107 .
AMA StyleLiang Xue, Xin Huang, Yuchun Wu, Xingchen Yan, Yan Zheng. Grade Setting of a Timber Logistics Center Based on a Complex Network: A Case Study of 47 Timber Trading Markets in China. Information. 2020; 11 (2):107.
Chicago/Turabian StyleLiang Xue; Xin Huang; Yuchun Wu; Xingchen Yan; Yan Zheng. 2020. "Grade Setting of a Timber Logistics Center Based on a Complex Network: A Case Study of 47 Timber Trading Markets in China." Information 11, no. 2: 107.
The information level of the urban public transport system is constantly improving, which promotes the use of smart cards by passengers. The OD (origination–destination) travel time of passengers reflects the temporal and spatial distribution of passenger flow. It is helpful to improve the flow efficiency of passengers and the sustainable development of the city. It is an urgent problem to select appropriate indexes to evaluate OD travel time and analyze the correlation of these indexes. More than one million OD records are generated by the AFC (Auto Fare Collection) system of Nanjing metro every day. A complex network method is proposed to evaluate and analyze OD travel time. Five working days swiping data of Nanjing metro are selected. Firstly, inappropriate data are filtered through data preprocessing. Then, the OD travel time indexes can be divided into three categories: time index, complex network index, and composite index. Time index includes use time probability, passenger flow between stations, average time between stations, and time variance between stations. The complex network index is based on two models: Space P and ride time, including the minimum number of rides, and the shortest ride time. Composite indicators include inter site flow efficiency and network flow efficiency. Based on the complex network model, this research quantitatively analyzes the Pearson correlation of the indexes of OD travel time. This research can be applied to other public transport modes in combination with big data of public smart cards. This will improve the flow efficiency of passengers and optimize the layout of the subway network and urban space.
Wei Yu; Xiaofei Ye; Jun Chen; Xingchen Yan; Tao Wang. Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method. Sustainability 2020, 12, 1113 .
AMA StyleWei Yu, Xiaofei Ye, Jun Chen, Xingchen Yan, Tao Wang. Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method. Sustainability. 2020; 12 (3):1113.
Chicago/Turabian StyleWei Yu; Xiaofei Ye; Jun Chen; Xingchen Yan; Tao Wang. 2020. "Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method." Sustainability 12, no. 3: 1113.
Walking, as a healthy and environmentally friendly mode of travel, has been revived in many cities around the world. The mid-block crosswalk (MBC) is a common type of pedestrian facility, and the pedestrian hybrid beacon (PHB) is one of most commonly used signalized MBCs, having a wide range of applications. This study applied an upstream detection (UD) strategy to PHB to reduce the pedestrian waiting time at the crossing. Data were collected through video recordings at two crosswalks at two different periods of the day in the city of Nanjing. First, basic simulation models were developed in VISSIM according to the current layout and the signal control methods of the two crosswalks. Second, signal control logic was adjusted to develop simulation models of PHB. Third, upstream detectors were added to develop simulation models of PHB with a UD strategy. Models for PHB were simulated with 10 different random seeds, and a paired t-test was conducted to evaluate the performance of the UD strategy statistically. The results show that the UD strategy for PHB reduces pedestrian waiting time and increases vehicle delay. However, the reduction in pedestrian waiting time is greater than the increase in vehicle delay. The UD strategy has also been found to be more effective for crosswalks, with relatively short crossing lengths and low pedestrian volume. Finally, a discussion about factors concerned with the application of UD strategies in practice is carried out.
Zhen Yang; Baojie Wang; Xingchen Yan; Jianxiao Ma; Wenyun Tang. Improving Pedestrian Hybrid Beacon Crosswalk by Using Upstream Detection Strategy. Journal of Advanced Transportation 2019, 2019, 1 -11.
AMA StyleZhen Yang, Baojie Wang, Xingchen Yan, Jianxiao Ma, Wenyun Tang. Improving Pedestrian Hybrid Beacon Crosswalk by Using Upstream Detection Strategy. Journal of Advanced Transportation. 2019; 2019 ():1-11.
Chicago/Turabian StyleZhen Yang; Baojie Wang; Xingchen Yan; Jianxiao Ma; Wenyun Tang. 2019. "Improving Pedestrian Hybrid Beacon Crosswalk by Using Upstream Detection Strategy." Journal of Advanced Transportation 2019, no. : 1-11.
Shared parking is not commonly applied in residential areas. The reason is that parking suppliers and managers believe that there are many uncertainties and conflicts in obtaining sharing benefits and taking sharing risks. To increase their acceptance of shared parking in residential areas, risk and benefit factors were identified by an influential analysis and a questionnaire survey. A research framework based on the structural equation model was developed to analyze the relationship between shared-parking risks, shared-parking benefits, management pressure, and intentions of parking suppliers and managers. The results showed that, to parking suppliers, the risks of shared parking have the largest effect on suppliers’ intention to apply shared parking by a standardized coefficient of −0.85, followed by the benefits of shared parking (0.29), and management pressures (−0.14). To the parking managers, management pressures have the largest effect on managers’ intention to apply shared parking by a standardized coefficient of −0.74, followed by the benefits of shared parking (0.52) and risks of shared parking (−0.46). These results can help in increasing parking suppliers’ and managers’ acceptance of shared parking in residential areas.
Jin Xie; Xiaofei Ye; Zhongzhen Yang; Xingchen Yan; Lili Lu; Zhen Yang; Tao Wang. Impact of Risk and Benefit on the Suppliers’ and Managers’ Intention of Shared Parking in Residential Areas. Sustainability 2019, 12, 268 .
AMA StyleJin Xie, Xiaofei Ye, Zhongzhen Yang, Xingchen Yan, Lili Lu, Zhen Yang, Tao Wang. Impact of Risk and Benefit on the Suppliers’ and Managers’ Intention of Shared Parking in Residential Areas. Sustainability. 2019; 12 (1):268.
Chicago/Turabian StyleJin Xie; Xiaofei Ye; Zhongzhen Yang; Xingchen Yan; Lili Lu; Zhen Yang; Tao Wang. 2019. "Impact of Risk and Benefit on the Suppliers’ and Managers’ Intention of Shared Parking in Residential Areas." Sustainability 12, no. 1: 268.
This study aimed to analyze the characteristics of bicycle–passenger conflicts at bus stops and develop a model to predict the number of conflicts accurately. This paper investigated the traffic flow operation at bus stops by video recording. Duration and distribution characteristics of bicycle–passenger conflicts were statistically analyzed. Then four types of conflicts defined based on evasive behavior (cyclist yielding as Type 1, cyclist bypassing as Type 2, passenger yielding as Type 3, and passenger bypassing as Type 4) were compared. A generalized event count (GEC) model was established for bicycle–passenger conflict estimation and analysis. The main results indicated that: (1) The average conflict duration was 1.716 s, whilst 60.9% of conflicts existed near the accesses of bus stops in longitudinal direction; (2) Type 1 conflict was significantly different from Type 2, 3, and 4 conflicts in duration, whilst the three had no significant difference; (3) the proposed GEC model showed good performance in predicting bicycle–passenger conflicts, with 15.71% of mean-absolute-percentage-error and 0.8772 of R2; and (4) bicycle volume, bus passenger volume, and passenger crossing time were critical factors impacting the number of bicycle–passenger conflicts. Finally, transport agencies may consider installing separations and crosswalks to improve the safety of the stop area.
Xingchen Yan; Tao Wang; Jun Chen; Xiaofei Ye; Zhen Yang; Hua Bai. Analysis of the Characteristics and Number of Bicycle–Passenger Conflicts at Bus Stops for Improving Safety. Sustainability 2019, 11, 5263 .
AMA StyleXingchen Yan, Tao Wang, Jun Chen, Xiaofei Ye, Zhen Yang, Hua Bai. Analysis of the Characteristics and Number of Bicycle–Passenger Conflicts at Bus Stops for Improving Safety. Sustainability. 2019; 11 (19):5263.
Chicago/Turabian StyleXingchen Yan; Tao Wang; Jun Chen; Xiaofei Ye; Zhen Yang; Hua Bai. 2019. "Analysis of the Characteristics and Number of Bicycle–Passenger Conflicts at Bus Stops for Improving Safety." Sustainability 11, no. 19: 5263.
Urban metro alleviates traffic pressure and also faces safety management problems. The metro AFC (Automatic Fare Collection System) records the OD (Origin-Destination) data of passengers’ daily trips. Many researches often neglect the pretreatment of data cleaning based on smart card data. Anomaly OD records also reflect the safety problems. How to use OD to identify anomalous data and passengers’ anomalous behavior is a research hotspot of metro big data. OD data of Nanjing metro were analyzed, and standard data cleaning processes were proposed including inbound records until the day before yesterday, inbound records of next days, negative records and overtime records. Then, using the data after cleaning, we analyze long-time records, short-time records, inbound and outbound records between the same stations, the swiping card records of more times, and carry out analysis. One day is chosen as an example to illustrate the analysis process, and then the OD records of several days are compared to summarize the classification of OD anomalies. Through analysis, OD anomalies can be classified into two categories: system anomalies and passenger behavior anomalies. System anomalies can be eliminated by upgrading. Abnormal passenger behavior reflects some potential safety problems. This research can effectively identify the abnormal behavior of passengers by tracking and comparing the appearing frequency of passenger cards. OD anomaly classification can be further refined, so that it has more practical value, can improve the level of metro safety management.
Wei Yu; Hua Bai; Jun Chen; Xingchen Yan. Anomaly Detection of Passenger OD on Nanjing Metro Based on Smart Card Big Data. IEEE Access 2019, 7, 138624 -138636.
AMA StyleWei Yu, Hua Bai, Jun Chen, Xingchen Yan. Anomaly Detection of Passenger OD on Nanjing Metro Based on Smart Card Big Data. IEEE Access. 2019; 7 (99):138624-138636.
Chicago/Turabian StyleWei Yu; Hua Bai; Jun Chen; Xingchen Yan. 2019. "Anomaly Detection of Passenger OD on Nanjing Metro Based on Smart Card Big Data." IEEE Access 7, no. 99: 138624-138636.